Liberate Your Time: Accelerate With R

class: center, middle, inverse, title-slide

.title[
# Liberate Your Time: Accelerate With R
]
.subtitle[
## Highlights and Examples
]
.author[
### Thomas Sharpe, <a href="mailto:thomassharpe@isu.edu" class="email">thomassharpe@isu.edu</a>
]
.date[
### AIR Forum: June 2023
]

---

# So Many Project Ideas, Not Enough Time!

.pull-left[

Fancy Visualizations (gganimate)

![](data:image/png;base64,#intro/population_animation.gif)

]

.pull-right[

Fancy Machine Learning (lots of R packages):

![](data:image/png;base64,#intro/clusplot.png)

]

<b> It can be quite hard to find the time to explore and enhance </b>

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# Objective

## Scope

- Demonstrate a few examples of leveraging a tool like R to speed up processes.

- Highlight key R packages and brief overview of the code flow.

- Spur curiosity and ideas.

- If you think, "Hey, I can do that better, or I might tweak that a bit." That's Great!

## Overview

- Getting Data
- Wrangling Data
- Text File Creation
- Excel File Creation
- Multiple HTML/PDF Files Creation
- Discussion and Q&A

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# College of Southern Idaho (CSI)

## Analytics and Institutional Research (AIR) Team

- FTE: 2.5 to 3.0
- Scope: Decision support, business intelligence, data-related compliance. Teamwork with IT on data governance.

## Implemented Software

- Jenzabar EX ERP / SIS
- Microsoft SQL Server
- No Enterprise Data Warehouse
- Power BI

<b> These solutions should be agnostic to the software used at most institutions </b>

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# Live Coding Session

![](data:image/png;base64,#intro/carlyton.gif)

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# Notes

## Limitations

- Reporting requirement changes.
- Business process changes.
- Software changes.

## Outcomes

This work has allowed for... (team effort big time!!!)

- Implementation of fairly wide-spread business intelligence usage

- Promptly keeping up with new requirements (e.g. NWCCU disaggregation) https://www.csi.edu/institutional-effectiveness/institutional-research/data-at-a-glance/default.aspx

- Predictive analytics and implementation with stakeholders.

- Beginning of data literacy training.

- Improving institution-wide data documentation.

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# Conclusion

<b> See conference material for what we worked through together.</b>

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# Discussion and Q&A